# Trajectory Tracking Using Cumulative Risk–Sensitive Finite Impulse Response Filters

## TL;DR

This paper introduces a new filter for trajectory tracking that improves accuracy and robustness in autonomous systems.

## Contribution

A novel cumulative risk–sensitive FIR filter is proposed for robust trajectory tracking in uncertain environments.

## Key findings

- The proposed filter significantly reduces average tracking error compared to existing methods.
- It demonstrates superior robustness in complex and dynamic environments.
- The algorithm is validated through comprehensive vehicle trajectory tracking experiments.

## Abstract

Trajectory tracking is a critical component of autonomous driving and robotic motion control. This paper proposes a novel robust finite impulse response (FIR) filter for linear time-invariant systems, aimed at enhancing the accuracy and robustness of trajectory tracking. To address the limitations of infinite impulse response (IIR) filters in complex environments, we integrate a cumulative risk–sensitive criterion with an FIR structure. The proposed filter effectively mitigates model mismatches and temporary modeling uncertainties, making it highly suitable for trajectory tracking in dynamic and uncertain environments. To validate its performance, a comprehensive vehicle trajectory tracking experiment is conducted. The experimental results demonstrate that, compared to the Kalman filter (KF), risk–sensitive filter (RSF), and unbiased FIR (UFIR) filter, the proposed algorithm significantly reduces the average tracking error and exhibits superior robustness in complex scenarios. This work provides a new and effective solution for trajectory tracking applications, with broad potential for practical implementation.